Certain industries, such as the real estate, retail, healthcare, finance, and logistics industries, generate millions of records daily, either through paper-based transactions, using standardized forms, or other documents. Employees expend significant manual labor to enter or update whatever information they are given into a computer system in a process called data entry. Given the volume of information a company receives, data entry can be a core part of the business. In retail, for example, a store may want to expedite the process of updating its product entry systems to know exactly what they can sell when they receive a shipment. Sometimes this depends on individualized entry or input after a person verifies each product received. By way of another example, in the healthcare industry, insurance companies may depend on the data entry of several medical insurance billing forms to determine what they are paying out at any particular time. In this particular case, often the employees are keying the data from an image of the source document adding additional complexity to the process.
Given the volume of records generated at any time, coupled with the need to track said records, there is an incredible need to eliminate or reduce as many errors as possible during data entry itself. These errors can range from human error resulting from fatigue such as unclear data entry to incomplete forms that do not provide all of the necessary data. In addition, errors can occur due to the low quality of the source document image, including but not limited to; scanning artifacts, printing on lines, or printing outside of target boxes. As errors continue, and as records continue to pile in, it becomes increasingly difficult to locate exactly where in the chain something went wrong, how to locate a record again, or where and how the issue originated.
To partially solve this data capture issue, optical character recognition/intelligent character recognition (OCR/ICR) is the electronic conversion of images of handwritten, printed, or typed text into machine-encoded text. This enables other machines or programs to read inputs from scanned documents, photographs, or text superimposed on an image. OCR/ICR enables the digitization of text so that it can be electronically edited or searched. Data entry processes have employed OCR/ICR to help read or store paper data records, normally for printouts of static data such as invoices, receipts, statements, or forms.
However, many documents are scanned and saved as an image. Standard OCR/ICR techniques struggle with these documents and can have a high failure conversion rate, particularly to when the images of the documents are warped or distorted in some way. A form may have both typed and handwritten information on it, which may make it difficult for OCR/ICR to differentiate between the two. Some documents may have been faxed, scanned, or scanned after being faxed before OCR/ICR was applied, leading to mixed results when recognizing the information in a document. For example, some field labels, lines, or shading may cause interference that OCR/ICR may not be able to interpret. This necessitates human data entry to manually extract this text.
Despite some advancements in OCR/ICR technology, OCR/ICR recognition rates have not increased to a rate where human intervention is not required. One of the key challenges is that document image quality levels vary across sources and time. To overcome issues like these, there is a need to properly prepare a document for OCR/ICR. Properly prepared documents may increase OCR/ICR effectiveness thus reducing the need for human intervention.